MUMBAI, India, March 13 -- Intellectual Property India has published a patent application (202621009153 A) filed by Lakshmi Narain College Of Technology, Bhopal, Madhya Pradesh, on Jan. 29, for 'a self-adaptive system for real-time load and stress optimization in industrial equipment.'
Inventor(s) include Dr. Hitesh Kumar; Dr. Anoop Kumar Pathariya; Dr. Ashok Kumar Rai; Dr. M Zahid Alam; Jatin Agarwal; Dr. Devendra Kumar Bajpai; Dr. Rakesh Agrawal; Dr. Shubha Agarwal; Dr. Anoop Kumar Chaturvedi; and Dr. Pratima Ojha.
The application for the patent was published on March 13, under issue no. 11/2026.
According to the abstract released by the Intellectual Property India: "The present invention relates to a self-adaptive system for real-time load and stress optimization in industrial equipment, intended to enhance operational efficiency, reliability, and safety. The system employs a plurality of multi-parameter sensors to continuously monitor critical operational variables including load, mechanical stress, vibration, temperature, torque, and rotational speed. The sensed data is collected in real time and processed to assess the operating condition of the equipment, enabling early detection of abnormal stress patterns and load imbalances that may lead to mechanical degradation or failure. The system further includes a processing unit coupled with an adaptive optimization engine that analyzes real-time and historical operational data to determine optimal control strategies. The optimization engine dynamically evaluates stress distribution, load variation, and performance efficiency using adaptive logic and feedback mechanisms. Based on this analysis, the system generates control signals to regulate operational parameters such as speed, torque, duty cycle, and load allocation, thereby minimizing excessive mechanical stress and improving energy efficiency during equipment operation. Additionally, the invention incorporates a closed-loop feedback framework that continuously monitors the impact of applied control actions and refines optimization decisions over time. This self-learning and autonomous capability allows the system to adapt to changing industrial conditions without manual intervention. As a result, the invention significantly reduces unplanned downtime, extends equipment service life, lowers maintenance costs, and supports intelligent, resilient industrial operations across manufacturing, energy, and heavy-machinery environments."
Disclaimer: Curated by HT Syndication.
